ArticleNAR genomics and bioinformatics2025
A method for estimating energy parameters of RNAs by differentiating base-pairing probabilities.
Article in NAR genomics and bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The structure of RNA is deeply related to its function, and information about RNA substructure energy parameters is useful for predicting its structure from its sequence. RNA in cells is often modified, and these various types of modifications affect its structure and function. In recent years, the use of pseudouridine modifications in RNA vaccines has increased the importance of predicting structures that include modified bases. However, energy parameters of substructures involving modified bases have not yet been sufficiently determined. Therefore, in this paper, we propose a method for inversely calculating energy parameters from base-pairing probabilities. This method optimizes energy parameters using the same mechanism as gradient descent in deep learning. We also propose efficient computational approaches, including the calculation of the derivative of the partition function using a dynamic programming method following computations with the McCaskill algorithm. Because base-pairing probabilities can be obtained by adjusting them through chemical probing methods, it is expected that parameter estimation can be performed without relying on labor-intensive experiments or molecular dynamics simulations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.